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All Journal Engineering Science Letter Jurnal Mahasiswa Manajemen, Bisnis, Enterpreneurship
Maniah
Universitas Logistik dan Bisnis Internasional
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Performance Evaluation of Machine Learning Algorithms for Supply Chain Data Classification Maniah
Engineering Science Letter Vol. 5 No. 01 (2026): Engineering Science Letter
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/IISTR.esl.001524

Abstract

Forecasting systems that are data-driven are of great importance in streamlining industrial and business processes during the digital transformation age. Supply chain management (SCM) is among the most significant processes for enhancing operational efficiency and supporting strategic decision-making. This study seeks to evaluate the performance of two machine learning-based classification algorithms, namely Naive Bayes and the k-Nearest Neighbours (K-NN) algorithm, using data in the supply chain. Some of the most valuable operational attributes, including payment method, customer segment, shipment status, profit per transaction, and customer location, are stored in the database. The data were first cleaned and then normalised and label-encoded, after which they were split into training and test sets with a ratio of 80:20. The performance of the two algorithms was assessed using accuracy, precision, recall, and F1-score. The findings of the research indicate that Naive Bayes is the most promising algorithm; its accuracy and precision are 99.75%, and its recall rate is close to 100% in the majority of the classes. These findings show that Naive Bayes is a probabilistic algorithm that better fits the data distribution than a distance-based K-NN algorithm.
FACTORS AFFECTING LOGISTICS PERFORMANCE IN THE EXPORT DIVISION (CASE STUDY OF PT. NIPPON EXPRESS INDONESIA) Nurul Fadhillahqurani Adhiputri; Maniah
JIMBIEN: JURNAL MAHASISWA MANAJEMEN, BISNIS, ENTREPRENEURSHIP Vol. 5 No. 1 (2026)
Publisher : Universitas Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36563/drbdp873

Abstract

The freight forwarding industry in Indonesia is expanding rapidly, yet research on the factors shaping logistics performance in this sector remains limited. This study examines the effects of Process Agility and Customer Retention on Logistics Performance in the export division of PT. Nippon Express Indonesia. Data were collected from 68 active customers using a 40-item questionnaire on a five-point Likert scale and analysed with variance-based Partial Least Squares Structural Equation Modelling (PLS-SEM) in SmartPLS 3.0. The measurement model showed adequate convergent and discriminant validity, with the majority of factor loadings above 0.70, AVE values above 0.50, and both the Fornell–Larcker and Heterotrait–Monotrait (HTMT) criteria satisfied. The structural model produced an R² of 0.435 for Logistics Performance, indicating moderate explanatory power. Both Customer Retention (β = 0.479) and Process Agility (β = 0.292) had positive, significant effects, with Customer Retention emerging as the stronger predictor. These findings extend the freight forwarding literature and offer practical guidance for strengthening logistics performance in competitive export markets.
Co-Authors Nurul Fadhillahqurani Adhiputri